Two crane species, whooping cranes (Grus americana) and sandhill cranes (Antigone canadensis), overwinter along the Texas Gulf Coast. Periodic, extreme drought conditions have prompted concerns that potential freshwater limitations could hinder conservation of cranes, especially endangered whooping cranes. In response, land managers constructed and maintained freshwater ponds in upland areas near saltmarshes on the wintering grounds. We monitored 30 of those constructed ponds using camera traps (1 Oct 2013–31 May 2014) to quantify crane visits. For each species, we modeled pond visits as a function of migration phenology and environmental variables at 2 scales. Pond-scale variables included distance to saltmarsh and monthly salinity, and broad-scale variables included bay salinity, drought index, and tide level. We found pond visits by both crane species followed migration phenology with the greatest pond use in January–February. Both crane species visited ponds more on the mainland than on Matagorda Island. Sandhill crane visits were fewer at ponds with higher salinities and those filled by well water. Cranes visited ponds during the diurnal period and tended to avoid visiting ponds during the first 10% of the day. Pond visits by whooping cranes were ≤0.15 times/pond/day and by sandhill cranes were ≤0.28 times/pond/day. Our results suggested crane visits to constructed ponds may not be as frequent as once assumed nor driven by tidal and salinity conditions in the bay. The greater number of crane visits to constructed ponds on the mainland compared to Matagorda Island may be related to shrub encroachment around natural freshwater swale wetlands on the mainland, which is not as prevalent of a problem on the island. With proper management, swales on the mainland may provide alternatives to constructed ponds for cranes to obtain freshwater and forage.
Yuma Ridgway’s rail (Rallus obsoletus yumanensis, hereafter, rail) is an endangered species for which patches of emergent marsh within the Salton Sea watershed comprise a substantial part of habitat for the species’ disjointed range in the southwestern United States. These areas of emergent marsh include (1) marshes managed by federal (particularly the U.S. Fish and Wildlife Service’s Sonny Bono Salton Sea National Wildlife Refuge), state (California Department of Fish and Wildlife), and local (Imperial Irrigation District) resource agencies that are sustained by direct deliveries of Colorado River water and (2) unmanaged marshes sustained by agricultural drainage water. Management of rail habitat in this arid environment is complicated by increasingly limited availability of unimpaired freshwater owing to water management decisions associated with the Quantification Settlement Agreement and risks posed by potentially harmful concentrations of selenium found in agricultural drainage water that can readily bioaccumulate in aquatic food webs.
To provide timely science for managers, herein we report summary statistics for managed and unmanaged emergent marshes sampled at the Salton Sea during the rail breeding season of 2016 pertaining to (1) selenium concentrations in food webs representing dietary pathways of selenium exposure and (2) patterns of rail occupancy and inter-marsh movements, estimated abundance, and regional population size of rail. For selenium-specific objectives, we sampled unfiltered surface water, midge larvae (Chironomidae), water boatmen (Corixidae), mosquitofish (Gambusia spp.), and crayfish (Astacidae). Selenium samples were collected from 15 fixed sampling points, each in managed and unmanaged marshes, during late February, April, and June 2016, which corresponded to rail pre-nesting, nesting, and fledgling reproductive life-stages, respectively. Two areas within the two treatment types (managed versus unmanaged marsh) were of particular interest to help assess risks associated with changing sea dynamics and different water-management strategies: (1) a large unmanaged marsh (Morton Bay) unintentionally created in approximately 2008 when it became separated from the Salton Sea as water inflows began to drop and a berm formed from accumulated sediment and (2) a restored marsh (HZ9A) managed by the Sonny Bono Salton Sea National Wildlife Refuge, which is currently supplied with Colorado River water but may be sustained in the future by a blend of clean (that is, low selenium) Colorado River and agricultural drainage water with higher selenium from the Alamo River. Hence, baseline data for these marshes are important for future management decisions. We also report selenium concentrations in rail blood, head feathers, and breast feathers from rails captured as part of the movement study. Results indicated relatively higher risks from dietary selenium exposure for rails occupying unmanaged marshes compared to managed marshes and similar risks among unmanaged marshes. However, risks also were potentially elevated for rails occupying some managed marshes (that is, the Hazard Marshes), where relatively high proportions of Chironomidae and mosquitofish exceeded dietary thresholds for selenium effects on avian reproduction.
For rail-specific objectives, we quantified occupancy and spatial distribution using call count data analyzed with imperfect detection models. Imperfect detection models allowed us to jointly estimate detection probability and abundance of detected rails in association with habitats. We then used estimates of detection probability and abundance at the habitat level to extrapolate rail population abundance for the Salton Sea region. Inter- and intra-marsh movements were described from over 5,000 locations obtained from 15 radio-marked rails. Resultant space use patterns indicated that, in general, selenium risk to individuals is not equally shared because of high levels of territoriality and very limited movement throughout the landscape. Moreover, the largest contiguous blocks of habitat are associated with unmanaged marshlands located on the former southeastern shoreline and outside traditional management areas and authorities. Thus, a substantial proportion of the rail population that is using unmanaged marsh on the southeastern shoreline may have disproportionate risk of elevated selenium exposure, yet how that risk translates to population-level effects remains unknown.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221045","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Ricca, M.A., Overton, C.T., Anderson, T.W., Merritt, A., Harrity, E., Matchett, E., and Casazza, M.L., 2022, Yuma Ridgway’s rail selenium exposure and occupancy within managed and unmanaged emergent marshes at the Salton Sea: U.S. Geological Survey Open-File Report 2022–1045, 49 p., https://doi.org/10.3133/ofr20221045.","productDescription":"Report: x, 49 p.; 2 Data Releases","numberOfPages":"49","onlineOnly":"Y","ipdsId":"IP-115651","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":400780,"rank":7,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1045/ofr20221045.xml"},{"id":501775,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113059.htm","linkFileType":{"id":5,"text":"html"}},{"id":400770,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9R39F33","text":"Selenium concentrations in Yuma Ridgway's Rails occupying managed and unmanaged emergent marshes at the Salton Sea","description":"Ricca, M.A, Overton, C.T., Anderson, T.W., Merritt, A., Harrity, E. Matchett, E., and Casazza, M.L., 2022, Selenium concentrations in Yuma Ridgway’s Rails occupying managed and unmanaged emergent marshes at the Salton Sea: U.S. Geological Survey data release, https://doi.org/10.5066/P9R39F33."},{"id":400769,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9JRP0L6","text":"Yuma Ridgway’s Rail (Rallus obsoletus yumanensis) Population Surveys, Rail Movement, and Potential Habitat at the Salton Sea of California","description":"Overton, C.T., Ricca, M.A., Anderson, T.W., Merritt, A.M., Harrity, E., Matchett, E.L., Casazza, M.L., 2022, Yuma Ridgway’s rail (Rallus obsoletus yumanensis) population surveys, rail movement, and potential habitat at the Salton Sea of California: U.S. Geological Survey data release, https://doi.org/10.5066/P9JRP0L6."},{"id":400768,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1045/images"},{"id":400767,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/ofr20221045/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":400766,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1045/ofr20221045.pdf","text":"Report","size":"8 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":400765,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1045/covrthb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Salton Sea","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -115.6365966796875,\n 33.128351191631566\n ],\n [\n -115.51849365234374,\n 33.128351191631566\n ],\n [\n -115.51849365234374,\n 33.30298618122413\n ],\n [\n -115.6365966796875,\n 33.30298618122413\n ],\n [\n -115.6365966796875,\n 33.128351191631566\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
Accelerating relative sea-level rise (RSLR) is threatening coastal wetlands. However, rising CO2 concentrations may also stimulate carbon sequestration and vertical accretion, counterbalancing RSLR. A coastal wetland dominated by a C3 plant species was exposed to ambient and elevated levels of CO2 in situ from 1987 to 2019 during which time ambient CO2 concentration increased 18% and sea level rose 23 cm. Plant production did not increase in response to gradually rising ambient CO2 concentration during this period. Elevated CO2 increased shoot production relative to ambient CO2 for the first two decades, but from 2005 to 2019, elevated CO2 stimulation of production was diminished. The decline coincided with increases in relative sea level above a threshold that hindered root productivity. While elevated CO2 stimulation of elevation gain has the potential to moderate the negative impacts of RSLR on tidal wetland productivity, benefits for coastal wetland resilience will diminish in the long term as rates of RSLR accelerate.
","language":"English","publisher":"AAAS","doi":"10.1126/sciadv.abn0054","usgsCitation":"Zhu, C., Langley, J.A., Ziska, L.H., Cahoon, D., and Megonigal, J.P., 2022, Accelerated sea-level rise is suppressing CO2 stimulation of tidal marsh productivity: A 33-year study: Science Advances, v. 8, no. 20, eabn0054, 7 p., https://doi.org/10.1126/sciadv.abn0054.","productDescription":"eabn0054, 7 p.","ipdsId":"IP-134602","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":447751,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1126/sciadv.abn0054","text":"External Repository"},{"id":401299,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"8","issue":"20","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Zhu, Chunwu","contributorId":292081,"corporation":false,"usgs":false,"family":"Zhu","given":"Chunwu","email":"","affiliations":[{"id":62828,"text":"Institute of Soil Science, Chinese Academy of Sciences","active":true,"usgs":false}],"preferred":false,"id":843796,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Langley, J. Adam","contributorId":292082,"corporation":false,"usgs":false,"family":"Langley","given":"J.","email":"","middleInitial":"Adam","affiliations":[{"id":12766,"text":"Villanova University","active":true,"usgs":false}],"preferred":false,"id":843797,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ziska, Lewis H.","contributorId":292083,"corporation":false,"usgs":false,"family":"Ziska","given":"Lewis","email":"","middleInitial":"H.","affiliations":[{"id":7171,"text":"Columbia University","active":true,"usgs":false}],"preferred":false,"id":843798,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cahoon, Donald R. 0000-0002-2591-5667","orcid":"https://orcid.org/0000-0002-2591-5667","contributorId":219657,"corporation":false,"usgs":true,"family":"Cahoon","given":"Donald","middleInitial":"R.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":843799,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Megonigal, J. Patrick","contributorId":288317,"corporation":false,"usgs":false,"family":"Megonigal","given":"J.","email":"","middleInitial":"Patrick","affiliations":[{"id":13510,"text":"Smithsonian Environmental Research Center","active":true,"usgs":false}],"preferred":false,"id":843800,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70231595,"text":"ofr20221024 - 2022 - Continuous stream discharge, salinity, and associated data collected in the lower St. Johns River and its tributaries, Florida, 2020","interactions":[],"lastModifiedDate":"2026-03-27T20:03:48.787042","indexId":"ofr20221024","displayToPublicDate":"2022-05-17T14:31:30","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-1024","displayTitle":"Continuous Stream Discharge, Salinity, and Associated Data Collected in the Lower St. Johns River and Its Tributaries, Florida, 2020","title":"Continuous stream discharge, salinity, and associated data collected in the lower St. Johns River and its tributaries, Florida, 2020","docAbstract":"The U.S. Army Corps of Engineers, Jacksonville District, is deepening the St. Johns River channel in Jacksonville, Florida, from 40 to 47 feet along 13 miles of the river channel beginning at the mouth of the river at the Atlantic Ocean, in order to accommodate larger, fully loaded cargo vessels. The U.S. Geological Survey, in cooperation with the U.S. Army Corps of Engineers, monitored stage, discharge, and (or) water temperature and salinity at 26 continuous data collection stations in the St. Johns River and its tributaries.
This is the fifth annual report by the U.S. Geological Survey on data collection for the Jacksonville Harbor deepening project. The report contains information pertinent to data collection during the 2020 water year, from October 2019 to September 2020. The addition of water-quality data collection at St. Johns River at Buffalo Bluff near Satsuma was the only modification to the previously installed network.
Discharge and salinity varied widely during the data collection period, which included above-average rainfall for 3 of the 5 counties in the study area. Total annual rainfall for all counties ranked third among the annual totals computed for the 5 years considered for this study. Annual mean discharge at Clapboard Creek was highest among the tributaries, followed by Ortega River, Durbin Creek, Pottsburg Creek at U.S. 90, Cedar River, Trout River, Julington Creek, Pottsburg Creek near South Jacksonville, Dunn Creek, and Broward River, whose annual mean was lowest. Annual mean discharge at 8 of the 10 tributary monitoring sites was higher for the 2020 water year than for the 2019 water year, and the computed annual mean flow at Clapboard Creek was the highest over the 5 years considered for this study. The annual mean discharge for each of the main-stem sites was higher for the 2020 water year than for the 2019 water year except for Buffalo Bluff, which remained the same.
Among the tributary sites, annual mean salinity was highest at Clapboard Creek, the site closest to the Atlantic Ocean, and was lowest at Durbin Creek, the site farthest from the ocean. Annual mean salinity data from the main-stem sites on the St. Johns River indicate that salinity decreased with distance upstream from the ocean, which was expected. Relative to annual mean salinity calculated for the 2019 water year, annual mean salinity at all monitoring locations was higher for the 2020 water year except at the tributary sites of Trout River, Dunn Creek, and Clapboard Creek, which were lower, and Durbin Creek, which remained the same. The 2020 annual mean salinity on the main-stem of the St. Johns River was the highest since the beginning of the study in 2016 at Dancy Point, Racy Point, Shands Bridge, below Shands Bridge, above Buckman Bridge, and Jacksonville (Acosta Bridge). Among the tributary sites, annual mean salinity rankings for 2020 were highest for Julington Creek and Ortega River, which were the second-highest on record for those sites.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221024","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers","usgsCitation":"Ryan, P.J., 2022, Continuous stream discharge, salinity, and associated data collected in the lower St. Johns River and its tributaries, Florida, 2020: U.S. Geological Survey Open-File Report 2022–1024, 48 p., https://doi.org/10.3133/ofr20221024.","productDescription":"Report: ix, 48 p.; Dataset","numberOfPages":"62","onlineOnly":"Y","ipdsId":"IP-133884","costCenters":[{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"links":[{"id":400657,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1024/coverthb.jpg"},{"id":400658,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1024/ofr20221024.pdf","text":"Report","size":"3.73 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1024"},{"id":400659,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1024/ofr20221024.XML"},{"id":400660,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1024/images"},{"id":400661,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":401171,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/ofr20221024/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":501767,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113057.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Florida","otherGeospatial":"St. Johns River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -82.27935791015625,\n 29.14736383122664\n ],\n [\n -80.38970947265625,\n 29.14736383122664\n ],\n [\n -80.38970947265625,\n 30.56226095049944\n ],\n [\n -82.27935791015625,\n 30.56226095049944\n ],\n [\n -82.27935791015625,\n 29.14736383122664\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Caribbean-Florida Water Science Center
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A cooperative study between the National Park Service (NPS) and the U.S. Geological Survey (USGS) characterized groundwater quality and hydrogeology in parts of Pinnacles National Park. The water-quality investigation assessed the geochemistry of springs, wells, surface water, and precipitation and analyzed geochemistry of rock formations that affect the water chemistry through water-rock interaction. The hydrogeology investigation used geophysical and groundwater level data to characterize groundwater-flow processes in the alluvial deposits of Bear Valley and the Chalone Creek watershed.
Analysis of aqueous geochemical parameters in water samples from perennial springs, water-supply wells, and surface waters was conducted for samples collected after the dry season (autumnal) and after the wet season (vernal) to assess changes in geochemistry due to changes in groundwater levels or flow resulting from precipitation. The chemistry of bulk precipitation collected during the wet season was also analyzed. Bedrock samples were analyzed for geochemical parameters to help constrain groundwater sources, flow paths, and weathering. The geochemical investigations show a correspondence between the source rock and the spring-water chemistry that can be attributed to the mineralogy of the source rock. The narrow range of strontium isotopes in water samples, sourced in geochemically and mineralogically disparate rocks, indicates that the bedrock groundwater is relatively old and has reached quasi-steady state with respect to weathering of susceptible minerals.
Groundwater-level monitoring indicated that the water table is shallow—from 0 to 10 meters (m) below land surface. In southern Bear Valley and in the Chalone Creek alluvium, water levels rose and declined by several meters over each annual cycle of this study. In northern Bear Valley, water levels rose modestly over two wet seasons but declined during a third wet season. In Bear Valley, groundwater/surface-water interaction occurs along the perennial reach of Sandy Creek. Groundwater discharges to the upstream part of the reach, becomes surface water and is partly consumed by evapotranspiration, and infiltrates farther downstream. In the Chalone Creek alluvium, runoff-generated surface-water flow in intermittent stream reaches is a major component of groundwater recharge. After the onset of significant streamflow, creek water rapidly recharges groundwater until water levels rise to nearly the creek level. Groundwater levels generally remain high throughout the wet season, then gradually decline after the creek becomes dry.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221026","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Scheiderich, K., Tiedeman, C.R., Hsieh, P.A., 2022, Aqueous geochemistry of waters and hydrogeology of alluvial deposits, Pinnacles National Park, California: U.S. Geological Survey Open-File Report 2022-1026, 39 p., https://doi.org/10.3133/ofr20221026.","productDescription":"Report: viii, 39 p.; 3 Data Releases","numberOfPages":"39","onlineOnly":"Y","ipdsId":"IP-129434","costCenters":[{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true}],"links":[{"id":400733,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9IZXRC0","text":"Streamflow data collected by the wading method, Pinnacles National Park, California, 2018","description":"Tiedeman, C.R., Ingebritsen, S.E., and Hsieh, P.A., 2021, Streamflow data collected by the wading method, Pinnacles National Park, California, 2018: U.S. Geological Survey data release, https://doi.org/10.5066/P9IZXRC0."},{"id":400732,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9AMDH71","text":"Passive Seismic Data Collected for the Horizontal-to-Vertical Spectral Ratio (HVSR) Method, Pinnacles National Park, California, 2018-2020","description":"Tiedeman, C.R., and Hsieh, P.A., 2021, Passive Seismic Data Collected for the Horizontal-to-Vertical Spectral Ratio (HVSR) Method, Pinnacles National Park, California, 2018-2020: U.S. Geological Survey data release, https://doi.org/10.5066/P9AMDH71."},{"id":400435,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1026/covrthb.jpg"},{"id":400731,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9BMM0XG","text":"Geochemistry of rocks, precipitation, and water sources from Pinnacles National Park, California, 2016-2017","description":"Scheiderich, K.D., 2021, Geochemistry of rocks, precipitation, and water sources from Pinnacles National Park, California, 2016-2017: U.S. Geological Survey data release, https://doi.org/10.5066/P9BMM0XG."},{"id":400436,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1026/ofr20221026.pdf","text":"Report","size":"8 MB","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"California","otherGeospatial":"Pinnacles National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.20666503906249,\n 36.4729263733008\n ],\n [\n -121.2070083618164,\n 36.458430880233415\n ],\n [\n -121.20237350463867,\n 36.45856894533104\n ],\n [\n -121.20254516601562,\n 36.444761218880885\n ],\n [\n -121.2070083618164,\n 36.44448503928196\n ],\n [\n -121.20769500732422,\n 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WMA- Laboratory & Analytical Services Division
U.S. Geological Survey
USGS Headquarters
12201 Sunrise Valley Drive
Reston, VA 20192
Producing accurate hindcasts and forecasts with coupled models is challenging due to complex parameterizations that are difficult to ground in observational data. We present a calibration workflow that utilizes a series of machine learning algorithms paired with Windsurf, a coupled beach-dune model (Aeolis, the Coastal Dune Model, and XBeach), to produce hindcasts and forecasts of morphologic change along Bogue Banks, North Carolina. Neural networks paired with genetic algorithms allow us to fine tune calibration parameters for the hindcast, and then a long short-term memory neural network, trained on the hindcast, produces a 4-year forecast. We compare our hindcasts to observations from 2016 to 2017 and find they successfully reproduce observed modes of dune and beach change except for seaward growth of the dune face. We compare our forecasts to observations from 2016 to 2020 and find that they produce reasonably accurate predictions of dune change except when there are significant instances of erosion during the forecast period.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.envsoft.2022.105404","usgsCitation":"Itzkin, M., Moore, L.J., Ruggiero, P., Hovenga, P.A., and Hacker, S.D., 2022, Combining process-based and data-driven approaches to forecast beach and dune change: Environmental Modelling & Software, v. 153, 105404, 14 p., https://doi.org/10.1016/j.envsoft.2022.105404.","productDescription":"105404, 14 p.","ipdsId":"IP-134588","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":487468,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.envsoft.2022.105404","text":"Publisher Index Page"},{"id":400757,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Carolina","otherGeospatial":"Bogue Banks","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -77.10479736328125,\n 34.6252978589571\n ],\n [\n -76.66534423828124,\n 34.6252978589571\n ],\n [\n -76.66534423828124,\n 34.74838307098529\n ],\n [\n -77.10479736328125,\n 34.74838307098529\n ],\n [\n -77.10479736328125,\n 34.6252978589571\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"153","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Itzkin, Michael 0000-0003-0693-0607","orcid":"https://orcid.org/0000-0003-0693-0607","contributorId":291846,"corporation":false,"usgs":true,"family":"Itzkin","given":"Michael","email":"","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":843218,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Moore, Laura J.","contributorId":195973,"corporation":false,"usgs":false,"family":"Moore","given":"Laura","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":843219,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ruggiero, Peter","contributorId":15709,"corporation":false,"usgs":false,"family":"Ruggiero","given":"Peter","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":843220,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hovenga, Paige A. 0000-0002-3569-0123","orcid":"https://orcid.org/0000-0002-3569-0123","contributorId":267191,"corporation":false,"usgs":false,"family":"Hovenga","given":"Paige","email":"","middleInitial":"A.","affiliations":[{"id":55435,"text":"College of Engineering, Oregon State University, Corvallis, OR, USA","active":true,"usgs":false}],"preferred":false,"id":843221,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hacker, Sally D.","contributorId":291847,"corporation":false,"usgs":false,"family":"Hacker","given":"Sally","email":"","middleInitial":"D.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":843222,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70234249,"text":"70234249 - 2022 - Hot spots and hot moments in the Critical Zone: Identification of and incorporation into reactive transport models","interactions":[],"lastModifiedDate":"2022-08-05T13:52:04.318024","indexId":"70234249","displayToPublicDate":"2022-05-17T08:46:59","publicationYear":"2022","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Hot spots and hot moments in the Critical Zone: Identification of and incorporation into reactive transport models","docAbstract":"Biogeochemical processes are often spatially discrete (hot spots) and temporally isolated (hot moments) due to variability in controlling factors like hydrologic fluxes, lithological characteristics, bio-geomorphic features, and external forcing. Although these hot spots and hot moments (HSHMs) account for a high percentage of carbon, nitrogen and nutrient cycling within the Critical Zone, the ability to identify and incorporate them into reactive transport models remains a significant challenge. This chapter provides an overview of the hot spots hot moments (HSHMs) concepts, where past work has largely focused on carbon and nitrogen dynamics within riverine systems. This work is summarized in the context of process-based and data-driven modeling approaches, including a brief description of recent research that casts a wider net to incorporate Hg, Fe and other Critical Zone elements, and focuses on interdisciplinary approaches and concepts. The broader goal of this chapter is to provide an overview of the gaps in our current understanding of HSHMs, and the opportunities therein, while specifically focusing on the underlying parameters and processes leading to their prognostic and diagnostic representation in reactive transport models.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Biogeochemistry of the Critical Zone","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Springer Nature","doi":"10.1007/978-3-030-95921-0_2","usgsCitation":"Arora, B., Briggs, M., Zarnetske, J.P., Stegen, J., Gomez-Velez, J., and Dwivedi, D., 2022, Hot spots and hot moments in the Critical Zone: Identification of and incorporation into reactive transport models, chap. of Biogeochemistry of the Critical Zone, p. 9-47, https://doi.org/10.1007/978-3-030-95921-0_2.","productDescription":"39 p.","startPage":"9","endPage":"47","ipdsId":"IP-114081","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":404874,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2022-05-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Arora, Bhavna 0000-0001-7841-886X","orcid":"https://orcid.org/0000-0001-7841-886X","contributorId":290532,"corporation":false,"usgs":false,"family":"Arora","given":"Bhavna","email":"","affiliations":[{"id":38900,"text":"Lawrence Berkeley National Laboratory","active":true,"usgs":false}],"preferred":false,"id":848330,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Briggs, Martin A. 0000-0003-3206-4132","orcid":"https://orcid.org/0000-0003-3206-4132","contributorId":222756,"corporation":false,"usgs":true,"family":"Briggs","given":"Martin","middleInitial":"A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":848331,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zarnetske, Jay P.","contributorId":210073,"corporation":false,"usgs":false,"family":"Zarnetske","given":"Jay","email":"","middleInitial":"P.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":848332,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stegen, James","contributorId":242792,"corporation":false,"usgs":false,"family":"Stegen","given":"James","affiliations":[{"id":48525,"text":"Earth and Biological Sciences Division, Pacific Northwest National Laboratory","active":true,"usgs":false}],"preferred":false,"id":848333,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gomez-Velez, Jesus","contributorId":219087,"corporation":false,"usgs":false,"family":"Gomez-Velez","given":"Jesus","affiliations":[{"id":36656,"text":"Vanderbilt University","active":true,"usgs":false}],"preferred":false,"id":848334,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Dwivedi, D.","contributorId":294554,"corporation":false,"usgs":false,"family":"Dwivedi","given":"D.","affiliations":[{"id":36254,"text":"LBNL","active":true,"usgs":false}],"preferred":false,"id":848335,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70234225,"text":"70234225 - 2022 - Velocity modeling of supercritical pore fluids through porous media under reservoir conditions with applications for petroleum secondary migration and carbon sequestration plumes","interactions":[],"lastModifiedDate":"2022-08-04T13:38:44.407482","indexId":"70234225","displayToPublicDate":"2022-05-17T08:31:43","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":11447,"text":"SEG-AAPG Interpretation","active":true,"publicationSubtype":{"id":10}},"title":"Velocity modeling of supercritical pore fluids through porous media under reservoir conditions with applications for petroleum secondary migration and carbon sequestration plumes","docAbstract":"Computational methods to characterize secondary migration in porous media traditionally rely on fluid transport equations with assumptions of time invariance, such as flowpath modeling of buoyancy vectors, statistical percolation algorithms, capillary pressure curves, or a form of Darcy’s Law which presumes instantaneous fluid transport. However, in petroleum systems modeling, the timeframe of secondary migration from source to reservoir is important to quantify in relation to other geologic factors such as timing of petroleum generation, fault movement, and seal formation. Additionally, quantifying migration velocities enables an estimation of the distance a plume of geologically sequestered carbon dioxide travels over time, as well as the identification of low-permeability strata appropriate for long-term containment. This study introduces a method to quantify transport velocities of supercritical fluids in low-permeability lithologies for a broad range of rock and fluid properties likely encountered in the sedimentary sequence. A time-dependent form of Darcy’s Law for pressure-driven viscous flow through homogeneous isotropic porous media was used to model flow velocities within a carrier bed. Thermodynamic equations of state were used to determine thermophysical properties of supercritical pore fluids under reservoir pressures ranging from 0–200 MPa (0–29,000 psi) to constrain the momentum equations. Three case studies were examined that (1) estimated fluid flow velocities of methane within the low-permeability Upper Jurassic Haynesville Formation, (2) defined permeability-based flow units to evaluate saline formations for long-term geologic carbon sequestration, and (3) calculated the migration distance of carbon dioxide plumes at the Decatur, Illinois injection and sequestration project.","language":"English","publisher":"Society of Economic Geologists","doi":"10.1190/int-2021-0182.1","usgsCitation":"Burke, L.A., 2022, Velocity modeling of supercritical pore fluids through porous media under reservoir conditions with applications for petroleum secondary migration and carbon sequestration plumes: SEG-AAPG Interpretation, v. 10, no. 3, p. SG1-SG9, https://doi.org/10.1190/int-2021-0182.1.","productDescription":"9 p.","startPage":"SG1","endPage":"SG9","ipdsId":"IP-126541","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":447759,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1190/int-2021-0182.1","text":"Publisher Index Page"},{"id":435846,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9GT9TWK","text":"USGS data release","linkHelpText":"Data tables associated with velocity modeling of supercritical pore fluids through porous media at reservoir conditions with applications for petroleum secondary migration and carbon sequestration plumes"},{"id":404814,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"10","issue":"3","noUsgsAuthors":false,"publicationDate":"2022-05-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Burke, Lauri A. 0000-0002-2035-8048 lburke@usgs.gov","orcid":"https://orcid.org/0000-0002-2035-8048","contributorId":3859,"corporation":false,"usgs":true,"family":"Burke","given":"Lauri","email":"lburke@usgs.gov","middleInitial":"A.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":848241,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70233241,"text":"70233241 - 2022 - Revealing active Mars with HiRISE digital terrain models","interactions":[],"lastModifiedDate":"2022-07-19T12:14:27.972653","indexId":"70233241","displayToPublicDate":"2022-05-17T07:09:52","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Revealing active Mars with HiRISE digital terrain models","docAbstract":"The clean energy transition will require a vast increase in metal supply, yet new mineral deposit discoveries are declining, due in part to challenges associated with exploring under sedimentary and volcanic cover. Recently, several case studies have demonstrated links between lithospheric electrical conductors imaged using magnetotelluric (MT) data and mineral deposits, notably Iron Oxide Copper Gold (IOCG). Adoption of MT methods for exploration is therefore growing but the general applicability and relationship with many other deposit types remains untested. Here, we compile a global inventory of MT resistivity models from Australia, North and South America, and China and undertake the first quantitative assessment of the spatial association between conductors and three mineral deposit types commonly formed in convergent margin settings. We find that deposits formed early in an orogenic cycle such as volcanic hosted massive sulfide (VHMS) and copper porphyry deposits show weak to moderate correlations with conductors in the upper mantle. In contrast, deposits formed later in an orogenic cycle, such as orogenic gold, show strong correlations with mid-crustal conductors. These variations in resistivity response likely reflect mineralogical differences in the metal source regions of these mineral systems and suggest a metamorphic-fluid source for orogenic gold is significant. Our results indicate the resistivity structure of mineralized convergent margins strongly reflects late-stage processes and can be preserved for hundreds of millions of years. Discerning use of MT is therefore a powerful tool for mineral exploration.
","language":"English","publisher":"Springer Nature","doi":"10.1038/s41598-022-11921-2","usgsCitation":"Kirkby, A., Czarnota, K., Huston, D.L., Champion, D.C., Doublier, M.P., Bedrosian, P.A., Duan, J., and Heinson, G., 2022, Lithospheric conductors reveal source regions of convergent margin mineral systems: Scientific Reports, v. 12, 8190, 10 p., https://doi.org/10.1038/s41598-022-11921-2.","productDescription":"8190, 10 p.","ipdsId":"IP-130445","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":501609,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-022-11921-2","text":"Publisher Index Page"},{"id":501583,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Australia, China","otherGeospatial":"North America, South America","volume":"12","noUsgsAuthors":false,"publicationDate":"2022-05-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Kirkby, Alison 0000-0003-1361-440X","orcid":"https://orcid.org/0000-0003-1361-440X","contributorId":222461,"corporation":false,"usgs":false,"family":"Kirkby","given":"Alison","email":"","affiliations":[{"id":35920,"text":"Geoscience Australia","active":true,"usgs":false}],"preferred":false,"id":957955,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Czarnota, Karol","contributorId":259291,"corporation":false,"usgs":false,"family":"Czarnota","given":"Karol","affiliations":[{"id":35920,"text":"Geoscience Australia","active":true,"usgs":false}],"preferred":false,"id":957956,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Huston, David L.","contributorId":259293,"corporation":false,"usgs":false,"family":"Huston","given":"David","middleInitial":"L.","affiliations":[{"id":35920,"text":"Geoscience Australia","active":true,"usgs":false}],"preferred":false,"id":957957,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Champion, David C.","contributorId":259290,"corporation":false,"usgs":false,"family":"Champion","given":"David","middleInitial":"C.","affiliations":[{"id":35920,"text":"Geoscience Australia","active":true,"usgs":false}],"preferred":false,"id":957958,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Doublier, Michael P.","contributorId":259292,"corporation":false,"usgs":false,"family":"Doublier","given":"Michael","middleInitial":"P.","affiliations":[{"id":35920,"text":"Geoscience Australia","active":true,"usgs":false}],"preferred":false,"id":957959,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bedrosian, Paul A. 0000-0002-6786-1038 pbedrosian@usgs.gov","orcid":"https://orcid.org/0000-0002-6786-1038","contributorId":839,"corporation":false,"usgs":true,"family":"Bedrosian","given":"Paul","email":"pbedrosian@usgs.gov","middleInitial":"A.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":957960,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Duan, Jinming","contributorId":367954,"corporation":false,"usgs":false,"family":"Duan","given":"Jinming","affiliations":[{"id":35920,"text":"Geoscience Australia","active":true,"usgs":false}],"preferred":false,"id":957961,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Heinson, Graham","contributorId":211596,"corporation":false,"usgs":false,"family":"Heinson","given":"Graham","email":"","affiliations":[{"id":36897,"text":"University of Adelaide","active":true,"usgs":false}],"preferred":false,"id":957962,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70232265,"text":"70232265 - 2022 - Global cycling and climate effects of aeolian dust controlled by biological soil crusts","interactions":[],"lastModifiedDate":"2022-06-21T16:35:28.748212","indexId":"70232265","displayToPublicDate":"2022-05-16T11:31:05","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2845,"text":"Nature Geoscience","active":true,"publicationSubtype":{"id":10}},"title":"Global cycling and climate effects of aeolian dust controlled by biological soil crusts","docAbstract":"Biological soil crusts (biocrusts) cover ~12% of the global land surface. They are formed by an intimate association between soil particles, photoautotrophic and heterotrophic organisms, and they effectively stabilize the soil surface of drylands. Quantitative information on the impact of biocrusts on the global cycling and climate effects of aeolian dust, however, is not available. Here, we combine the currently limited experimental data with a global climate model to investigate the effects of biocrusts on regional and global dust cycling under current and future conditions. We estimate that biocrusts reduce the global atmospheric dust emissions by ~60%, preventing the release of ~0.7 Pg dust per year. Until 2070, biocrust coverage is expected to be severely reduced by climate change and land-use intensification. The biocrust loss will cause an increased dust burden, leading to a reduction of the global radiation budget of around 0.12 to 0.22 W m−2, corresponding to about 50% of the total direct forcing of anthropogenic aerosols. This biocrust control on dust cycling and its climate impacts have important implications for human health, biogeochemical cycling and the functioning of the ecosystems, and thus should be considered in the modelling, mitigation and management of global change.
","language":"English","publisher":"Springer Nature","doi":"10.1038/s41561-022-00942-1","usgsCitation":", R., Stanelle, T., Egerer, S., Cheng, Y., Suess, H.E., Canton, Y., Belnap, J., Andreae, M.O., Tegen, I., Reick, C., Poschl, U., and Weber, B., 2022, Global cycling and climate effects of aeolian dust controlled by biological soil crusts: Nature Geoscience, v. 15, p. 458-463, https://doi.org/10.1038/s41561-022-00942-1.","productDescription":"5 p.","startPage":"458","endPage":"463","ipdsId":"IP-137868","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":447771,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41561-022-00942-1","text":"Publisher Index Page"},{"id":402400,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"15","noUsgsAuthors":false,"publicationDate":"2022-05-16","publicationStatus":"PW","contributors":{"authors":[{"text":" Rodriguez-Caballero","contributorId":292505,"corporation":false,"usgs":false,"given":"Rodriguez-Caballero","email":"","affiliations":[{"id":62919,"text":"Agronomy Dept., University of Almeria, Carretera Sacramento s/n, 04120 La 6 Cañada de San Urbano (Almería), Spain; Multiphase Chemistry Department, Max Planck Institute for Chemistry, Hahn-8 Meitner-Weg 1, 55128 Mainz, Germany","active":true,"usgs":false}],"preferred":false,"id":844907,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stanelle, T","contributorId":292506,"corporation":false,"usgs":false,"family":"Stanelle","given":"T","email":"","affiliations":[{"id":62920,"text":"Institute for Atmospheric and Climate Science, ETH Zurich, Universitätstrasse 16, 10 8092 Zürich, Switzerland; Now at: Department of Waste, Water, Energy and Air, Canton of Zurich, Walcheplatz 12 2, 8090 Zurich, Switzerland","active":true,"usgs":false}],"preferred":false,"id":844908,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Egerer, S","contributorId":292507,"corporation":false,"usgs":false,"family":"Egerer","given":"S","email":"","affiliations":[{"id":62921,"text":"Climate Service Center Germany (GERICS), Fischertwiete 1, 20095 Hamburg, 14 Germany; Max Planck Institute for Meteorology, Bundesstraße 53, 20146 Hamburg, Germany","active":true,"usgs":false}],"preferred":false,"id":844909,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cheng, Yang","contributorId":211352,"corporation":false,"usgs":false,"family":"Cheng","given":"Yang","email":"","affiliations":[],"preferred":false,"id":844910,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Suess, H. E.","contributorId":69292,"corporation":false,"usgs":false,"family":"Suess","given":"H.","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":844911,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Canton, Y","contributorId":292508,"corporation":false,"usgs":false,"family":"Canton","given":"Y","affiliations":[{"id":62922,"text":"Agronomy Dept, Univ of Almeria, Carretera Sacramento s/n, 04120 La 6 Cañada de San Urbano (Almería), Spain; Centro de Investigación de Colecciones Científicas de la Universidad de Almería,(Almería) Spain","active":true,"usgs":false}],"preferred":false,"id":844912,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Belnap, Jayne 0000-0001-7471-2279 jayne_belnap@usgs.gov","orcid":"https://orcid.org/0000-0001-7471-2279","contributorId":1332,"corporation":false,"usgs":true,"family":"Belnap","given":"Jayne","email":"jayne_belnap@usgs.gov","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":844913,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Andreae, M O","contributorId":292509,"corporation":false,"usgs":false,"family":"Andreae","given":"M","email":"","middleInitial":"O","affiliations":[{"id":62923,"text":"Max Planck Institute for Chemistry, Hahn-Meitner-Weg 1, 55128 Mainz, Germany; Scripps Institution of Oceanography, Univ of California San Diego, La Jolla, CA 92093, USA; Dept of Geology and Geophysics, King Saud Univ, Riyadh, Saudi Arabia","active":true,"usgs":false}],"preferred":false,"id":844914,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Tegen, I","contributorId":292510,"corporation":false,"usgs":false,"family":"Tegen","given":"I","email":"","affiliations":[{"id":62924,"text":"Institute for Tropospheric Research, Permoserstraße 15, 04318 Leipzig, Germany","active":true,"usgs":false}],"preferred":false,"id":844915,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Reick, C","contributorId":292511,"corporation":false,"usgs":false,"family":"Reick","given":"C","email":"","affiliations":[{"id":62925,"text":"Max Planck Institute for Meteorology, Bundesstraße 53, 20146 Hamburg, Germany","active":true,"usgs":false}],"preferred":false,"id":844916,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Poschl, Ulrich","contributorId":205642,"corporation":false,"usgs":false,"family":"Poschl","given":"Ulrich","email":"","affiliations":[{"id":37132,"text":"Multiphase Chemistry Department, Max Planck Institute for Chemistry, Hahn-Meitner-Weg 1, 55128 Mainz, Germany","active":true,"usgs":false}],"preferred":false,"id":844917,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Weber, B.","contributorId":197862,"corporation":false,"usgs":false,"family":"Weber","given":"B.","email":"","affiliations":[],"preferred":false,"id":844918,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70234165,"text":"70234165 - 2022 - Friction in clay-bearing faults increases with the ionic radius of interlayer cations","interactions":[],"lastModifiedDate":"2022-08-02T12:04:29.583516","indexId":"70234165","displayToPublicDate":"2022-05-16T07:00:54","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":8956,"text":"Communications Earth & Environment","active":true,"publicationSubtype":{"id":10}},"title":"Friction in clay-bearing faults increases with the ionic radius of interlayer cations","docAbstract":"Smectite can dramatically reduce the strength of crustal faults and may cause creep on natural faults without great earthquakes; however, the frictional mechanism remains unexplained. Here, our shear experiments reveal systematic increase in shear strength with the increase of the ionic radius of interlayer cations among lithium-, sodium-, potassium-, rubidium-, and cesium-montmorillonites, a smectite commonly found in faults. Using density-functional-theory calculations, we find that relatively small sodium ions fit in the ditrigonal cavities on the montmorillonite surfaces, resulting in weakening of interlayer repulsion during sliding. On the other hand, relatively large potassium ions do not fit in the ditrigonal cavities, resulting in a larger resistance to sliding due to electrostatic repulsion between potassium ions. Calculated shear strength is consistent with our shear experiments by considering the partial dehydration of the frictional contact area. These results provide the basis for developing a quantitative model of smectite-bearing fault rheology.
Identifying animal behaviors, life history states, and movement patterns is a prerequisite for many animal behavior analyses and effective management of wildlife and habitats. Most approaches classify short-term movement patterns with high frequency location or accelerometry data. However, patterns reflecting life history across longer time scales can have greater relevance to species biology or management needs, especially when available in near real-time. Given limitations in collecting and using such data to accurately classify complex behaviors in the long-term, we used hourly GPS data from 5 waterfowl species to produce daily activity classifications with machine-learned models using “automated modelling pipelines”.
Automated pipelines are computer-generated code that complete many tasks including feature engineering, multi-framework model development, training, validation, and hyperparameter tuning to produce daily classifications from eight activity patterns reflecting waterfowl life history or movement states. We developed several input features for modeling grouped into three broad categories, hereafter “feature sets”: GPS locations, habitat information, and movement history. Each feature set used different data sources or data collected across different time intervals to develop the “features” (independent variables) used in models.
Automated modelling pipelines rapidly developed easily reproducible data preprocessing and analysis steps, identification and optimization of the best performing model and provided outputs for interpreting feature importance. Unequal expression of life history states caused unbalanced classes, so we evaluated feature set importance using a weighted F1-score to balance model recall and precision among individual classes. Although the best model using the least restrictive feature set (only 24 hourly relocations in a day) produced effective classifications (weighted F1 = 0.887), models using all feature sets performed substantially better (weighted F1 = 0.95), particularly for rarer but demographically more impactful life history states (i.e., nesting).
Automated pipelines generated models producing highly accurate classifications of complex daily activity patterns using relatively low frequency GPS and incorporating more classes than previous GPS studies. Near real-time classification is possible which is ideal for time-sensitive needs such as identifying reproduction. Including habitat and longer sequences of spatial information produced more accurate classifications but incurred slight delays in processing.
","language":"English","publisher":"Springer Nature","doi":"10.1186/s40462-022-00324-7","usgsCitation":"Overton, C.T., Casazza, M.L., Bretz, J., McDuie, F., Matchett, E., Mackell, D.A., Lorenz, A., Mott, A., Herzog, M.P., and Ackerman, J.T., 2022, Machine learned daily life history classification using low frequency tracking data and automated modelling pipelines: Application to North American waterfowl: Movement Ecology, v. 10, 23, 13 p., https://doi.org/10.1186/s40462-022-00324-7.","productDescription":"23, 13 p.","ipdsId":"IP-133430","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":447785,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s40462-022-00324-7","text":"Publisher Index Page"},{"id":435848,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XBZKZ8","text":"USGS data release","linkHelpText":"Hourly GPS Locations, Associated Habitat 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0000-0003-3657-5941","orcid":"https://orcid.org/0000-0003-3657-5941","contributorId":222610,"corporation":false,"usgs":true,"family":"Lorenz","given":"Austen","email":"","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":857613,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Mott, Andrea 0000-0001-9586-9590","orcid":"https://orcid.org/0000-0001-9586-9590","contributorId":299367,"corporation":false,"usgs":false,"family":"Mott","given":"Andrea","affiliations":[{"id":64822,"text":"USGS WERC (name not found)","active":true,"usgs":false}],"preferred":false,"id":857614,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Herzog, Mark P. 0000-0002-5203-2835 mherzog@usgs.gov","orcid":"https://orcid.org/0000-0002-5203-2835","contributorId":131158,"corporation":false,"usgs":true,"family":"Herzog","given":"Mark","email":"mherzog@usgs.gov","middleInitial":"P.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":857615,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Ackerman, Joshua T. 0000-0002-3074-8322","orcid":"https://orcid.org/0000-0002-3074-8322","contributorId":202848,"corporation":false,"usgs":true,"family":"Ackerman","given":"Joshua","middleInitial":"T.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":857616,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70256672,"text":"70256672 - 2022 - Wildlife associates of nine-banded armadillo (Dasypus novemcinctus) burrows in Arkansas","interactions":[],"lastModifiedDate":"2024-08-30T14:24:25.771746","indexId":"70256672","displayToPublicDate":"2022-05-15T09:16:03","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Wildlife associates of nine-banded armadillo (Dasypus novemcinctus) burrows in Arkansas","title":"Wildlife associates of nine-banded armadillo (Dasypus novemcinctus) burrows in Arkansas","docAbstract":"The Nine-banded Armadillo (Dasypus novemcinctus) is a widespread burrowing species with an expanding geographic range across the southeastern and midwestern United States. Armadillos dig numerous, large burrows within their home ranges and these burrows are likely used by a diverse suite of wildlife species as has been reported for other burrowing ecosystem engineers such as Gopher Tortoises (Gopherus polyphemus), Desert Tortoises (Gopherus agassizi), and Black-tailed Prairie Dogs (Cynomys ludovicianus). We used motion-triggered game cameras at 35 armadillo burrows in 4 ecoregions of Arkansas and documented 19 species of mammals, 4 species of reptile, 1 species of amphibian, and 40 species of bird interacting with burrows. Bobcat (Lynx rufus), Coyote (Canis latrans), Eastern Cottontail (Sylvilagus floridanus), Gray Fox (Urocyon cinereoargenteus), Gray Squirrel (Sciurus carolinensis), Northern Raccoon (Procyon lotor), Virginia Opossum (Didelphis virginiana), and unidentified rodents (mice and rats) were documented using burrows in all four ecoregions. We documented wildlife hunting, seeking shelter, rearing young in, and taking over and modifying armadillo burrows. The rate of use was highest in the Mississippi Alluvial Valley, a landscape dominated by agriculture, where natural refugia may be limited and rodents are abundant. Armadillo burrows are clearly visited and used by numerous wildlife species to fulfill various life stage requirements, and this list will likely expand if more attention is devoted to understanding the role of armadillos burrows. Armadillos are important ecosystem engineers, and their ecological role warrants more investigation and attention as opposed to only being viewed and managed as agricultural and garden pests.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.8858","usgsCitation":"DeGregorio, B.A., Veon, J.T., and Massey, A., 2022, Wildlife associates of nine-banded armadillo (Dasypus novemcinctus) burrows in Arkansas: Ecology and Evolution, v. 12, no. 5, e8858, 10 p., https://doi.org/10.1002/ece3.8858.","productDescription":"e8858, 10 p.","ipdsId":"IP-139270","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":447786,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.8858","text":"Publisher Index Page"},{"id":433366,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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Scientists are evaluated and promoted in their jobs and professional communities on the basis of their recognized contributions to science. Unlike a financial contribution, a scientific contribution is difficult to measure. Traditionally, credit for scientific contributions has been given through authorship and citations in scientific literature as well as awards and the naming of geographic features, instruments, and methods and other honorifics. However, these practices do not capture the breadth and depth of the contributions by all actors in modern, open science.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2022EO220239","usgsCitation":"Parsons, M.A., Katz, D.S., Langseth, M., Ramapriyan, H., and Ramdeen, S., 2022, Credit where credit is due: Eos Science News, HTML Document, https://doi.org/10.1029/2022EO220239.","productDescription":"HTML Document","ipdsId":"IP-143013","costCenters":[{"id":38128,"text":"Science Analytics and Synthesis","active":true,"usgs":true}],"links":[{"id":447787,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2022eo220239","text":"Publisher Index Page"},{"id":406349,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Parsons, Mark A. 0000-0002-7723-0950","orcid":"https://orcid.org/0000-0002-7723-0950","contributorId":296275,"corporation":false,"usgs":false,"family":"Parsons","given":"Mark","email":"","middleInitial":"A.","affiliations":[{"id":36730,"text":"University of Alabama","active":true,"usgs":false}],"preferred":false,"id":851123,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Katz, Daniel S. 0000-0001-5934-7525","orcid":"https://orcid.org/0000-0001-5934-7525","contributorId":296276,"corporation":false,"usgs":false,"family":"Katz","given":"Daniel","email":"","middleInitial":"S.","affiliations":[{"id":16984,"text":"University of Illinois at Urbana-Champaign","active":true,"usgs":false}],"preferred":false,"id":851124,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Langseth, Madison 0000-0002-4472-9106 mlangseth@usgs.gov","orcid":"https://orcid.org/0000-0002-4472-9106","contributorId":191744,"corporation":false,"usgs":true,"family":"Langseth","given":"Madison","email":"mlangseth@usgs.gov","affiliations":[{"id":38128,"text":"Science Analytics and Synthesis","active":true,"usgs":true}],"preferred":true,"id":851125,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ramapriyan, Hampapuram 0000-0002-8425-8943","orcid":"https://orcid.org/0000-0002-8425-8943","contributorId":296277,"corporation":false,"usgs":false,"family":"Ramapriyan","given":"Hampapuram","email":"","affiliations":[{"id":7239,"text":"Science Systems and Applications, Inc.","active":true,"usgs":false}],"preferred":false,"id":851126,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ramdeen, Sarah 0000-0003-1135-5942","orcid":"https://orcid.org/0000-0003-1135-5942","contributorId":296278,"corporation":false,"usgs":false,"family":"Ramdeen","given":"Sarah","email":"","affiliations":[{"id":7171,"text":"Columbia University","active":true,"usgs":false}],"preferred":false,"id":851127,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70231492,"text":"sim3487 - 2022 - Geologic maps of the Stephenson and Winchester quadrangles, Frederick and Clarke Counties, Virginia, and Inwood and White Hall quadrangles, Berkeley and Jefferson Counties, West Virginia","interactions":[],"lastModifiedDate":"2026-04-01T15:15:19.750245","indexId":"sim3487","displayToPublicDate":"2022-05-13T11:20:00","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3487","displayTitle":"Geologic Maps of the Stephenson and Winchester Quadrangles, Frederick and Clarke Counties, Virginia, and Inwood and White Hall Quadrangles, Berkeley and Jefferson Counties, West Virginia","title":"Geologic maps of the Stephenson and Winchester quadrangles, Frederick and Clarke Counties, Virginia, and Inwood and White Hall quadrangles, Berkeley and Jefferson Counties, West Virginia","docAbstract":"The study area consists of four contiguous 7.5-minute quadrangles and is located in Frederick and Clarke Counties, Virginia, and Berkeley and Jefferson Counties, West Virginia. The individual quadrangles are Stephenson, Winchester, Inwood, and White Hall. The study area lies within the Great Valley subprovince of the Valley and Ridge physiographic province where about 23,000 feet (ft) (7,000 meters [m]) of Middle Cambrian to Upper Devonian sedimentary rocks are exposed and are overlain by Holocene and older surficial deposits. The area of the four maps is divided into three geologic regions based on the following primary lithologies: (1) Cambrian and Ordovician carbonate rocks of the Great Valley southeast of the North Mountain fault zone and east and west of the core of the Massanutten synclinorium; (2) shale, graywacke, and calcareous shale of the Ordovician Martinsburg Formation of the Great Valley and Massanutten synclinorium; and (3) Ordovician through Devonian clastic rocks and minor limestone and dolostone northwest of and within the North Mountain fault zone. Rocks of all three regions were folded and faulted during the late Paleozoic Alleghanian orogeny (roughly 320 to 250 million years before present). The terrain of this portion of the Great Valley generally is gently to moderately rolling with low local relief with elevations in the study area ranging from about 425 ft (130 m) where Opequon Creek flows out of the eastern edge of the Inwood quadrangle to about 950 ft (290 m) adjacent to Round Hill in the western part of the Winchester quadrangle. Sinkholes and other karst features are common in the carbonate rocks of the Great Valley. The area west of the North Mountain fault zone is underlain by middle Paleozoic strata and consists of a series of ridges and valleys with higher local relief, with elevations ranging from about 785 ft (240 m) in the vicinity of Green Spring in the central part of the White Hall quadrangle to about 1,435 ft (437 m) at the summit of North Mountain in the northeastern part of the White Hall quadrangle.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3487","usgsCitation":"Weary, D.J., Doctor, D.H., and Orndorff, R.C., 2022, Geologic maps of the Stephenson and Winchester quadrangles, Frederick and Clarke Counties, Virginia, and Inwood and White Hall quadrangles, Berkeley and Jefferson Counties, West Virginia: U.S. Geological Survey Scientific Investigations Map 3487, 4 sheets, scale 1:24,000, 33-p. pamphlet, https://doi.org/10.3133/sim3487.","productDescription":"Pamphlet: viii, 33 p.; 4 Sheets: 28.00 x 42.00 inches or smaller; Base Map; Metadata; Database; Read Me","numberOfPages":"33","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-009285","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":501931,"rank":9,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113056.htm","linkFileType":{"id":5,"text":"html"}},{"id":400509,"rank":8,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/sim/3487/sim3487_openaccess.zip","text":"Open Access","size":"10.5 MB","linkFileType":{"id":6,"text":"zip"}},{"id":400508,"rank":7,"type":{"id":23,"text":"Spatial Data"},"url":"https://pubs.usgs.gov/sim/3487/sim3487_basemaps.zip","text":"Base Maps","size":"540 MB","linkFileType":{"id":6,"text":"zip"}},{"id":400507,"rank":6,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3487/sim3487_mapsheets.zip","text":"Map Sheets 1–4","size":"604 MB","linkFileType":{"id":6,"text":"zip"}},{"id":400502,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3487/coverthb3.jpg"},{"id":400505,"rank":4,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sim/3487/sim3487_metadata.zip","text":"Metadata","size":"108 KB","linkFileType":{"id":6,"text":"zip"}},{"id":400506,"rank":5,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/sim/3487/sim3487_readme.txt","text":"Read Me","size":"8.50 KB","linkFileType":{"id":2,"text":"txt"}},{"id":400504,"rank":3,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/sim/3487/sim3487_database.zip","text":"Database","size":"36.2 MB","linkFileType":{"id":6,"text":"zip"}},{"id":400503,"rank":2,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/sim/3487/sim3487_pamphlet.pdf","text":"Pamphlet","size":"9.18 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3487"}],"country":"United States","state":"Virginia, West Virginia","county":"Berkeley County, Clarke County, Frederick County, Jefferson County","otherGeospatial":"Inwood, Stephenson, White Hall and Winchester quadrangles","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -78.25,\n 39.375\n ],\n [\n -78,\n 39.375\n ],\n [\n -78,\n 39.125\n ],\n [\n -78.25,\n 39.125\n ],\n [\n -78.25,\n 39.375\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Florence Bascom Geoscience Center
U.S. Geological Survey
926A National Center
12201 Sunrise Valley Drive
Reston, VA 20192
To examine controls on the local stress field at Augustine Volcano, Alaska, before its 2006 eruption, we calculated fault plane solutions for volcano-tectonic earthquakes from 2002 to 2006. The P-axis orientation was first aligned to the regional maximum compression (NW) and then rotated by about 90° (perpendicular to the dike alignment) after the onset of surface deformation in mid-August 2005. Using 3D finite element models, we systematically evaluated the effects of tectonic stresses, volcanic edifice densities, and dike overpressures on the local stress field orientation. Combining data and models to generate “phase diagrams” of different stress controls by these competing effects, we argue that moderate tectonic stress of 2–3 MPa at 600 m above sea level slightly exceeded the edifice loading before the precursory deformation and was then overprinted by a local stress field from dike opening with an overpressure of ~15 MPa.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2022GL097958","usgsCitation":"Zhan, Y., Roman, D., Le Mevel, H., and Power, J., 2022, Earthquakes indicated stress field change during the 2006 unrest of Augustine Volcano, Alaska: Geophysical Research Letters, v. 49, e2022GL097958, 9 p., https://doi.org/10.1029/2022GL097958.","productDescription":"e2022GL097958, 9 p.","ipdsId":"IP-137090","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":463353,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Augustine Volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -153.5968722856842,\n 59.431514142471286\n ],\n [\n -153.5968722856842,\n 59.29604332497132\n ],\n [\n -153.3209313297999,\n 59.29604332497132\n ],\n [\n -153.3209313297999,\n 59.431514142471286\n ],\n [\n -153.5968722856842,\n 59.431514142471286\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"49","noUsgsAuthors":false,"publicationDate":"2022-05-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Zhan, Yan","contributorId":345673,"corporation":false,"usgs":false,"family":"Zhan","given":"Yan","email":"","affiliations":[{"id":82691,"text":"Carnegie Institution for Science, Washington, DC","active":true,"usgs":false}],"preferred":false,"id":917229,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Roman, Diana","contributorId":237832,"corporation":false,"usgs":false,"family":"Roman","given":"Diana","affiliations":[{"id":47620,"text":"Dept. of Terrestrial Magnetism, Carnegie Institution for Science, Washington DC 20015","active":true,"usgs":false}],"preferred":false,"id":917230,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Le Mevel, Helene","contributorId":345674,"corporation":false,"usgs":false,"family":"Le Mevel","given":"Helene","affiliations":[{"id":82691,"text":"Carnegie Institution for Science, Washington, DC","active":true,"usgs":false}],"preferred":false,"id":917231,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Power, John 0000-0002-7233-4398","orcid":"https://orcid.org/0000-0002-7233-4398","contributorId":215240,"corporation":false,"usgs":true,"family":"Power","given":"John","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":917232,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70237692,"text":"70237692 - 2022 - Evaluating aromatization of solid bitumen generated in the presence and absence of water: Implications for solid bitumen reflectance as a thermal proxy","interactions":[],"lastModifiedDate":"2022-10-19T13:22:32.108904","indexId":"70237692","displayToPublicDate":"2022-05-13T08:20:18","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2033,"text":"International Journal of Coal Geology","active":true,"publicationSubtype":{"id":10}},"title":"Evaluating aromatization of solid bitumen generated in the presence and absence of water: Implications for solid bitumen reflectance as a thermal proxy","docAbstract":"Geological models for petroleum generation suggest thermal conversion of oil-prone sedimentary organic matter in the presence of water promotes increased liquid saturate yield, whereas absence of water causes formation of an aromatic, cross-linked solid bitumen residue. To test the influence of hydrogen from water, organic-rich (22 wt% total organic carbon, TOC) mudrock samples from the Eocene lacustrine Green River Formation Mahogany zone oil shale were pyrolyzed under hydrous and anhydrous conditions in closed system batch reactors at temperatures between 300 and 370 °C for 72 h. Pre- and post-pyrolysis samples were characterized using petrographic approaches including optical microscopy, reflectance, Raman spectroscopy, and scanning electron and transmission electron microscopy to quantify differences in relative appearance, abundance, and composition of solid bitumen newly generated during the pyrolysis experiments. Petrographic analyses were supplemented by geochemical screening measurements (TOC content and programmed temperature pyrolysis). Results show post-hydrous pyrolysis residues contain lower TOC, are comprised of solid bitumen with greater aromaticity, and have textures indicative of lower viscosities, relative to anhydrous residues from the same temperature pyrolysis conditions. These observations suggest solid bitumen forming from thermal conversion of oil-prone sedimentary organic matter under anhydrous conditions may be less aromatic, although more cross-linked, than solid bitumen forming under hydrous conditions at the same time-temperature combination. To explain these results, we suggest that a radical disproportionation mechanism is favored in the presence of hydrogen donated from water, and that this disproportionation promotes aromatization in the solid residue with concomitant expulsion of saturated hydrocarbons.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.coal.2022.104016","usgsCitation":"Hackley, P.C., Jubb, A., Smith, P.L., McAleer, R.J., Valentine, B.J., Hatcherian, J.J., Botterell, P.J., and Birdwell, J.E., 2022, Evaluating aromatization of solid bitumen generated in the presence and absence of water: Implications for solid bitumen reflectance as a thermal proxy: International Journal of Coal Geology, v. 258, 104016, 16 p., https://doi.org/10.1016/j.coal.2022.104016.","productDescription":"104016, 16 p.","ipdsId":"IP-136449","costCenters":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":447793,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.coal.2022.104016","text":"Publisher Index Page"},{"id":408537,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"258","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hackley, Paul C. 0000-0002-5957-2551 phackley@usgs.gov","orcid":"https://orcid.org/0000-0002-5957-2551","contributorId":592,"corporation":false,"usgs":true,"family":"Hackley","given":"Paul","email":"phackley@usgs.gov","middleInitial":"C.","affiliations":[{"id":255,"text":"Energy Resources Program","active":true,"usgs":true},{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":855030,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jubb, Aaron M. 0000-0001-6875-1079","orcid":"https://orcid.org/0000-0001-6875-1079","contributorId":201978,"corporation":false,"usgs":true,"family":"Jubb","given":"Aaron M.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":855031,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Smith, Patrick L.","contributorId":298071,"corporation":false,"usgs":false,"family":"Smith","given":"Patrick","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":855032,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McAleer, Ryan J. 0000-0003-3801-7441 rmcaleer@usgs.gov","orcid":"https://orcid.org/0000-0003-3801-7441","contributorId":215498,"corporation":false,"usgs":true,"family":"McAleer","given":"Ryan","email":"rmcaleer@usgs.gov","middleInitial":"J.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":855033,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Valentine, Brett J. 0000-0002-8678-2431 bvalentine@usgs.gov","orcid":"https://orcid.org/0000-0002-8678-2431","contributorId":3846,"corporation":false,"usgs":true,"family":"Valentine","given":"Brett","email":"bvalentine@usgs.gov","middleInitial":"J.","affiliations":[{"id":255,"text":"Energy Resources Program","active":true,"usgs":true},{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":855034,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hatcherian, Javin J. 0000-0001-9151-6798 jhatcherian@usgs.gov","orcid":"https://orcid.org/0000-0001-9151-6798","contributorId":195770,"corporation":false,"usgs":true,"family":"Hatcherian","given":"Javin","email":"jhatcherian@usgs.gov","middleInitial":"J.","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":855035,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Botterell, Palma J. 0000-0001-7140-0915 pjarboe@usgs.gov","orcid":"https://orcid.org/0000-0001-7140-0915","contributorId":5805,"corporation":false,"usgs":true,"family":"Botterell","given":"Palma","email":"pjarboe@usgs.gov","middleInitial":"J.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":855036,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Birdwell, Justin E. 0000-0001-8263-1452 jbirdwell@usgs.gov","orcid":"https://orcid.org/0000-0001-8263-1452","contributorId":3302,"corporation":false,"usgs":true,"family":"Birdwell","given":"Justin","email":"jbirdwell@usgs.gov","middleInitial":"E.","affiliations":[{"id":255,"text":"Energy Resources Program","active":true,"usgs":true},{"id":569,"text":"Southwest Climate Science Center","active":true,"usgs":true},{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":855037,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70231655,"text":"70231655 - 2022 - Using a multi-model ensemble approach to determine biodiversity hotspots with limited occurrence data in understudied areas: An example using freshwater mussels in México","interactions":[],"lastModifiedDate":"2022-05-19T12:22:18.441838","indexId":"70231655","displayToPublicDate":"2022-05-13T07:20:00","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Using a multi-model ensemble approach to determine biodiversity hotspots with limited occurrence data in understudied areas: An example using freshwater mussels in México","docAbstract":"Species distribution models (SDMs) are an increasingly important tool for conservation particularly for difficult-to-study locations and with understudied fauna. Our aims were to (1) use SDMs and ensemble SDMs to predict the distribution of freshwater mussels in the Pánuco River Basin in Central México; (2) determine habitat factors shaping freshwater mussel occurrence; and (3) use predicted occupancy across a range of taxa to identify freshwater mussel biodiversity hotspots to guide conservation and management. In the Pánuco River Basin, we modeled the distributions of 11 freshwater mussel species using an ensemble approach, wherein multiple SDM methodologies were combined to create a single ensemble map of predicted occupancy. A total of 621 species-specific observations at 87 sites were used to create species-specific ensembles. These predictive species ensembles were then combined to create local diversity hotspot maps. Precipitation during the warmest quarter, elevation, and mean temperature were consistently the most important discriminatory environmental variables among species, whereas land use had limited influence across all taxa. To the best of our knowledge, our study is the first freshwater mussel-focused research to use an ensemble approach to determine species distribution and predict biodiversity hotspots. Our study can be used to guide not only current conservation efforts but also prioritize areas for future conservation and study.
Green Lake, a deep mesotrophic lake located in a primarily agricultural watershed in central Wisconsin, USA, has experienced annual metalimnetic oxygen minima since the early 20th century. However, the severity of the phenomenon has increased over time, and late-summer dissolved oxygen (DO) concentrations have typically been <3 mg L−1 in recent years. In situ, high-frequency observations of oxygen depletion at multiple depths reveal that while DO consumption during stratification occurs most rapidly in the metalimnion, there is synchrony between DO time series extending into the hypolimnion. A biochemical oxygen demand-based modeling approach suggests that much of the relationship between water depth and respiration rates can be explained by differences in water temperature. The amount of labile organic matter present throughout the water column at the onset of stratification seems to be a primary determinant of the severity of the annual metalimnetic DO minimum in late summer. Productivity has increased in the lake as a result of increased nutrient loading and is the likely driver of the decrease in minimum DO concentrations. In addition, the onset and duration of stratification is an important factor in determining the severity of the DO minimum.
Effective conservation requires understanding species' abundance patterns and demographic rates across space and time. Ideally, such knowledge should be available for whole communities, as variation in species' dynamics can elucidate factors leading to biodiversity losses. However, collecting data to simultaneously estimate abundance and demographic rates is often prohibitively time-intensive and expensive for communities of species. We developed a “multi-species dynamic N-occupancy model” to estimate unbiased, community-wide relative abundance and demographic rates. Our model uses detection-nondetection data (e.g., repeated presence-absence surveys) to estimate both species- and community-level parameters as well as the effects of environmental factors. We conducted a simulation study that validated our modeling framework, demonstrating how and when such an approach can be valuable. Using data from a network of camera traps across tropical equatorial Africa, we then used our model to evaluate the statuses and trends of a forest-dwelling antelope community. We estimated relative abundance, rates of recruitment (i.e., reproduction and immigration), and apparent survival probabilities for each species' local population. Our analysis indicated that the antelope community was fairly stable in this region (although 17% of populations [species-park combinations] declined over the study period), with variation in apparent survival linked more closely to differences among national parks rather than individual species' life histories. The multi-species dynamic N-occupancy model requires only detection-nondetection data to evaluate the population dynamics of multiple sympatric species and can thus be a valuable tool for conservation efforts seeking to understand the reasons behind recent biodiversity loss.
","language":"English","publisher":"Society for Conservation Biology","doi":"10.1111/cobi.13934","usgsCitation":"Farr, M.T., O’Brien, T.O., Yackulic, C., and Zipkin, E.F., 2022, Quantifying the conservation status and abundance trends of wildlife communities with detection-nondetection data: Conservation Biology, v. 36, no. 6, e13934, 11 p., https://doi.org/10.1111/cobi.13934.","productDescription":"e13934, 11 p.","ipdsId":"IP-131250","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":447800,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/cobi.13934","text":"Publisher Index Page"},{"id":402056,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"36","issue":"6","noUsgsAuthors":false,"publicationDate":"2022-08-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Farr, Matthew T","contributorId":292414,"corporation":false,"usgs":false,"family":"Farr","given":"Matthew","email":"","middleInitial":"T","affiliations":[{"id":62897,"text":"Dept. of Integrative Biology, Michigan State University, East Lansing, MI 48824; Ecology, Evolution, and Behavior Program, Michigan State University, East Lansing, MI 48824","active":true,"usgs":false}],"preferred":false,"id":844497,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"O’Brien, Timothy O","contributorId":292415,"corporation":false,"usgs":false,"family":"O’Brien","given":"Timothy","email":"","middleInitial":"O","affiliations":[{"id":62898,"text":"Wildlife Conservation Society, Global Conservation Program, Bronx, NY","active":true,"usgs":false}],"preferred":false,"id":844498,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Yackulic, Charles B. 0000-0001-9661-0724","orcid":"https://orcid.org/0000-0001-9661-0724","contributorId":218825,"corporation":false,"usgs":true,"family":"Yackulic","given":"Charles","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":844499,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Zipkin, Elise F. 0000-0003-4155-6139","orcid":"https://orcid.org/0000-0003-4155-6139","contributorId":192755,"corporation":false,"usgs":false,"family":"Zipkin","given":"Elise","email":"","middleInitial":"F.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":844500,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70231509,"text":"sir20215022 - 2022 - Estimating stream temperature in the Willamette River Basin, northwestern Oregon—A regression-based approach","interactions":[],"lastModifiedDate":"2026-04-01T15:57:20.052617","indexId":"sir20215022","displayToPublicDate":"2022-05-12T12:56:11","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-5022","displayTitle":"Estimating Stream Temperature in the Willamette River Basin, Northwestern Oregon—A Regression-Based Approach","title":"Estimating stream temperature in the Willamette River Basin, northwestern Oregon—A regression-based approach","docAbstract":"The alteration of thermal regimes, including increased temperatures and shifts in seasonality, is a key challenge to the health and survival of federally protected cold-water salmonids in streams of the Willamette River basin in northwestern Oregon. To better support threatened fish species, the U.S. Army Corps of Engineers (USACE) and other water managers seek to improve the thermal regime in the Willamette River and key tributaries downstream of USACE dams by utilizing strategically timed flow releases from USACE dams. To inform flow management decisions, regression relations were developed for 12 Willamette River basin locations below USACE dams relating stream temperature with streamflow and air temperature utilizing publicly available datasets spanning 2000–18. The resulting relations provide simple tools to investigate stream temperature responses to changes in streamflow and climatic conditions in the Willamette River system.
Regression relations on the Willamette River and key tributaries show that, at locations sufficiently distant from the direct temperature influence of upstream dam releases, air temperature and streamflow are reasonable proxies to predict the 7-day average of the daily mean (7dADMean) and 7-day average of the daily maximum (7dADMax) water temperature with errors generally ≤1 degrees Celsius (°C). To account for seasonal variations in the relation between air temperature, streamflow, and stream temperature, a transition-smoothed, seasonal regression approach was used. Stream temperature is inversely correlated with streamflow in all seasons except “winter” (January–March), when it is relatively independent. Stream temperature is positively correlated with air temperature in all seasons, but the slope decreases at very low or very high air temperatures. Generally, fit is best for seasonal models “winter” (January–March), “spring” (April–May), “summer” (June–August), and “early autumn” (September–October). Error in “autumn” (November–December) is larger, probably due to variation in the onset timing of winter storms.
Simulated results from a climatological analysis of predicted stream temperature suggest that, excluding extremes and accounting for some seasonal variability, the 7dADMean and 7dADMax stream temperature sensitivity to air temperature and streamflow varies by location on the river. To investigate the potential range of stream temperature variability based on historical air temperature and streamflow conditions, stream temperature predictions were calculated using synthetic time series comprised of daily temperature values representing the 0.10, 0.33, 0.50, 0.67, and 0.90 quantile of air temperature and streamflow from 1954 (the year meaningful streamflow augmentation began) to 2018. Results show that from a “very hot” (0.90 quantile) and “very dry” (0.10 quantile) year to a “very cool” (0.10 quantile) and “very wet” (0.90; all quantiles from 1954 to 2018) year, the stream temperature sensitivity to air temperature and streamflow is about 3 °C at Harrisburg (river mile 161.0) and increases to about 5 °C at Keizer (river mile 82.2). While the number of days exceeding regulatory criteria are fewer in cooler, wetter years than in warmer, dryer years, the models suggest that the Willamette River will likely continue to exceed the State of Oregon maximum water-temperature criterion of 18 °C for sustained periods from late spring to early autumn and that the flow management practices evaluated in this study, while effective at influencing stream temperature, likely cannot prevent many or all such exceedances.
As modeled for 2018, a representative very hot year with normal to below-normal streamflow, stream temperature sensitivity to changes in streamflow of ±100 to ±1000 cubic feet per second produced mean monthly temperature changes from 0.0 to 1.4 °C at Keizer, Albany, and Harrisburg during summer. For a specified change in flow, temperature sensitivity is greater at upstream locations where streamflow is less than that at downstream locations because the change in streamflow is a greater percentage of total streamflow at upstream locations. Similarly, temperature response to a set change in flow is greater in the summer and early autumn low-flow season than in spring when flows are higher. The regression models developed in this study thus indicate that flow management is likely to have a greater effect on stream temperature at upstream locations (such as Harrisburg or Albany) and during the low-flow season than at downstream locations (such as Keizer) or during periods of higher streamflow.
","largerWorkType":{"id":18,"text":"Report"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215022","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers, Portland District","usgsCitation":"Stratton Garvin, L.E., Rounds, S.A., and Buccola, N.L., 2022, Estimating stream temperature in the Willamette River Basin, northwestern Oregon—A regression-based approach: U.S. Geological Survey Scientific Investigations Report 2021–5022, 40 p., https://doi.org/10.3133/sir20215022.","productDescription":"Report: viii, 40 p.; Data Release","numberOfPages":"40","onlineOnly":"Y","ipdsId":"IP-119336","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":501948,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113055.htm","linkFileType":{"id":5,"text":"html"}},{"id":400563,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9PALKQZ","text":"USGS Data Release","description":"Stratton Garvin, L.E., 2022, Stream temperature predic tions for the Willamette River Basin, northwestern Oregon estimated from regression equations (1954–2018): U.S. Geological Survey data release, https://doi.org/10.5066/P9PALKQZ.","linkHelpText":"Stream temperature predictions for the Willamette River Basin, northwestern Oregon estimated from regression equations (1954–2018)"},{"id":400560,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5022/sir20215022.pdf","text":"Report","size":"8.5 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":400559,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5022/covrthb.jpg"},{"id":400561,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5022/sir20215022.xml"},{"id":400562,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5022/images"}],"country":"United States","state":"Oregon","otherGeospatial":"Willamette River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.64013671874999,\n 43.54854811091286\n ],\n [\n -122.18994140624999,\n 43.54854811091286\n ],\n [\n -122.18994140624999,\n 45.99696161820381\n ],\n [\n -123.64013671874999,\n 45.99696161820381\n ],\n [\n -123.64013671874999,\n 43.54854811091286\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
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U.S. Geological Survey
2130 SW 5th Avenue
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The 15 January 2022 climactic eruption of Hunga volcano, Tonga, produced an explosion in the atmosphere of a size that has not been documented in the modern geophysical record. The event generated a broad range of atmospheric waves observed globally by various ground-based and spaceborne instrumentation networks. Most prominent was the surface-guided Lamb wave (≲0.01 hertz), which we observed propagating for four (plus three antipodal) passages around Earth over 6 days. As measured by the Lamb wave amplitudes, the climactic Hunga explosion was comparable in size to that of the 1883 Krakatau eruption. The Hunga eruption produced remarkable globally detected infrasound (0.01 to 20 hertz), long-range (~10,000 kilometers) audible sound, and ionospheric perturbations. Seismometers worldwide recorded pure seismic and air-to-ground coupled waves. Air-to-sea coupling likely contributed to fast-arriving tsunamis. Here, we highlight exceptional observations of the atmospheric waves.
","language":"English","doi":"10.1126/science.abo7063","usgsCitation":"Matoza, R.S., Fee, D., Assink, J.D., Iezzi, A., Green, D.N., Kim, K., Toney, L., Lecocq, T., Krishnamoorthy, S., Lalande, J., Nishida, K., Gee, K.L., Haney, M.M., Ortiz, H.D., Brissaud, Q., Martire, L., Rolland, L., Vergados, P., Nippress, A., Park, J., Shani-Kadmiel, S., Witsil, A., Arrowsmith, S., Caudron, C., Watada, S., Perttu, A., Taisne, B., Mialle, P., Le Pichon, A., Vergoz, J., Hupe, P., Blom, P.S., Waxler, R.M., De Angelis, S., Snively, J., Ringler, A.T., Anthony, R.E., Jolly, A., Kilgour, G., Averbuch, G., Ripepe, M., Ichihara, M., Arciniega-Ceballos, A., Astafyeva, E., Ceranna, L., Cevuard, S., Che, I., de Negri Leiva, R., Ebeling, C.W., Evers, L.G., Franco-Marin, L.E., Gabrielson, T., Hafner, K., Harrison, R.G., Komjathy, A., Lacanna, G., Lyons, J.J., Macpherson, K.A., Marchetti, E., McKee, K., Mellors, R., Mendo-Perez, G., Mikesell, T.D., Munaibari, E., Oyola-Merced, M., Park, I., Pilger, C., Ramos, C., Ruiz, M., Sabatini, R., Schwaiger, H., Tailpied, D., Talmadge, C., Vidot, J., Webster, J., and Wilson, D.C., 2022, Atmospheric waves and global seismoacoustic observations of the January 2022 Hunga eruption, Tonga: Science, v. 377, no. 6601, p. 95-100, https://doi.org/10.1126/science.abo7063.","productDescription":"6 p.","startPage":"95","endPage":"100","ipdsId":"IP-138527","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":447809,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://hdl.handle.net/11603/26621","text":"External Repository"},{"id":406673,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Tonga","otherGeospatial":"Hunga volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n 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Some facility operators curtail when individual birds have flight characteristics (e.g. altitude, distance or relative bearing of a bird's flight path) that exceed some threshold value, but thresholds currently in use have not been empirically evaluated. Overly restrictive thresholds can cause turbine curtailment for birds that never enter rotor-swept zones, thereby resulting in excess power loss. We evaluated the probability that birds, specifically eagles, entered the rotor-swept zone (hereafter ‘entry probability’) in response to their flight characteristics. We used an automated monitoring system to classify individuals as eagles or non-eagles and record flight paths of purported eagles at a wind facility in Wyoming, USA. We used logistic regression with occupancy dynamics and a distance-dependent colonization process to model entry probability. As a result, this model allowed entry probability to decrease with horizontal distance to the nearest turbine. The probability of entry varied with distance to the nearest turbine and approached zero when that distance was more than 202 m. Entry probability peaked when eagles flew 89 m above ground, corresponding to hub heights of turbines (80 m), and decreased to near-zero at altitudes of 189 m or more. Entry probabilities were greatest when flight paths were near the rotor-swept zone and when eagles flew slowly toward the nearest turbine. Compass bearing of a flight path was not associated with entry probability. Our model accurately forecasted entry probability in Wyoming (area under the curve (AUC) = 0.96) and was transferable to another facility in California, USA (AUC = 0.97); therefore, our results may be applicable across a variety of settings. Curtailment criteria can be based on flight path characteristics to forecast entry into rotor-swept zones. The use of distance and altitude thresholds when making curtailment decisions is justified. However, this analysis suggests alteration of the time to collision threshold, with curtailment initiated at greater distances as the speed of the bird decreases. Our novel modelling method and our results can inform curtailment criteria in any situation where curtailment decisions are made in real-time.
","language":"English","publisher":"Wiley","doi":"10.1111/ibi.13076","usgsCitation":"Rolek, B.W., Braham, M., Miller, T.A., Duerr, A.E., Katzner, T., McCabe, J.D., Dunn, L., and McClure, C.J., 2022, Flight characteristics forecast entry by eagles into rotor-swept zones of wind turbines: Ibis, v. 164, no. 4, p. 968-980, https://doi.org/10.1111/ibi.13076.","productDescription":"13 p.","startPage":"968","endPage":"980","ipdsId":"IP-136356","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":447812,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/ibi.13076","text":"Publisher Index Page"},{"id":401974,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","county":"Converse 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Brian W.","contributorId":200318,"corporation":false,"usgs":false,"family":"Rolek","given":"Brian","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":844398,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Braham, Melissa A.","contributorId":140127,"corporation":false,"usgs":false,"family":"Braham","given":"Melissa A.","affiliations":[{"id":12432,"text":"West Virginia University","active":true,"usgs":false}],"preferred":false,"id":844399,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Miller, Tricia A.","contributorId":190591,"corporation":false,"usgs":false,"family":"Miller","given":"Tricia","email":"","middleInitial":"A.","affiliations":[{"id":16210,"text":"Division of Forestry and Natural Resources, West Virginia University","active":true,"usgs":false}],"preferred":false,"id":844400,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Duerr, Adam E.","contributorId":190590,"corporation":false,"usgs":false,"family":"Duerr","given":"Adam","email":"","middleInitial":"E.","affiliations":[{"id":16210,"text":"Division of Forestry and Natural Resources, West Virginia University","active":true,"usgs":false}],"preferred":false,"id":844401,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Katzner, Todd E. 0000-0003-4503-8435 tkatzner@usgs.gov","orcid":"https://orcid.org/0000-0003-4503-8435","contributorId":191353,"corporation":false,"usgs":true,"family":"Katzner","given":"Todd E.","email":"tkatzner@usgs.gov","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":844402,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"McCabe, Jennifer D.","contributorId":264224,"corporation":false,"usgs":false,"family":"McCabe","given":"Jennifer","email":"","middleInitial":"D.","affiliations":[{"id":54406,"text":"The Peregrine Fund, Boise, Idaho","active":true,"usgs":false}],"preferred":false,"id":844403,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Dunn, Leah","contributorId":217944,"corporation":false,"usgs":false,"family":"Dunn","given":"Leah","email":"","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":844404,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"McClure, Christopher J.W.","contributorId":264223,"corporation":false,"usgs":false,"family":"McClure","given":"Christopher","email":"","middleInitial":"J.W.","affiliations":[{"id":54406,"text":"The Peregrine Fund, Boise, Idaho","active":true,"usgs":false}],"preferred":false,"id":844405,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70231653,"text":"70231653 - 2022 - Estimating occupancy from autonomous recording unit data in the presence of misclassifications and detection heterogeneity","interactions":[],"lastModifiedDate":"2022-08-15T13:51:56.290092","indexId":"70231653","displayToPublicDate":"2022-05-12T07:23:39","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2717,"text":"Methods in Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Estimating occupancy from autonomous recording unit data in the presence of misclassifications and detection heterogeneity","docAbstract":"1. Autonomous Recording Units (ARUs) are now widely used to survey communities of species. These surveys generate spatially and temporally replicated counts of unmarked animals, but such data typically include false negatives and misclassified detections, both of which may vary across sites in proportion to abundance. These data challenges can bias estimates of occupancy, and the typical approach of verifying individual detections is expensive.
2. We developed a Bayesian implementation of a two-species, false-positive N-mixture model for estimating occupancy from ARU data or other counts of unmarked animals that does not require manual verification. The model accounts for species misclassification and abundance-induced detection heterogeneity, as well as false negatives. To evaluate this model, we simulated 200 data sets for each of 29 scenarios, including scenarios in which misclassifications outnumbered correct classifications for rare species. We also applied the model to acoustic surveys of bats conducted on Fort Carson Army Post and Piñon Canyon Maneuver Site, Colorado, USA.
3. In the simulation study, bias, coverage, and root mean square error for occupancy estimates obtained from the two-species false-positive N-mixture model were superior to metrics obtained from two competing two-species false-positive occupancy models. Across 29 scenarios, absolute bias was consistently low (range: -0.03–0.07), while coverage averaged 93% (range: 74%–98%). For alternative occupancy models, absolute bias was often high (range: -0.36–0.39), and coverage averaged from 47%–65%. Although our model included an abundance parameter, abundance estimates were not reliable. For two species of Myotis bats, we estimated that 1%–5% of field-recorded detections were misclassified. Estimated occupancy (0.91 and 0.76) was lower than naïve estimates (1.00 and 0.94). Competing occupancy models implausibly estimated local occupancy of 0.00 at sites with numerous detections.
4. Our two-species, false-positive N-mixture model is significant because it accounts for detection heterogeneity and improves occupancy estimates without expensive manual verification of detections. Our field application indicated that misclassifications were not common, yet affected occupancy inferences. Given that ARUs are increasingly used to survey a broad range of taxa, such an occupancy model could be widely useful.
","language":"English","publisher":"Wiley","doi":"10.1111/2041-210X.13895","usgsCitation":"Clement, M., Royle, A., and Mixan, R., 2022, Estimating occupancy from autonomous recording unit data in the presence of misclassifications and detection heterogeneity: Methods in Ecology and Evolution, v. 13, no. 8, p. 1719-1729, https://doi.org/10.1111/2041-210X.13895.","productDescription":"11 p.","startPage":"1719","endPage":"1729","ipdsId":"IP-139383","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":447816,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/2041-210x.13895","text":"Publisher Index Page"},{"id":400804,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"13","issue":"8","noUsgsAuthors":false,"publicationDate":"2022-05-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Clement, Matt","contributorId":291855,"corporation":false,"usgs":false,"family":"Clement","given":"Matt","email":"","affiliations":[{"id":62776,"text":"AZ fish and game","active":true,"usgs":false}],"preferred":false,"id":843247,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Royle, J. Andrew 0000-0003-3135-2167 aroyle@usgs.gov","orcid":"https://orcid.org/0000-0003-3135-2167","contributorId":146229,"corporation":false,"usgs":true,"family":"Royle","given":"J. Andrew","email":"aroyle@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":843250,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mixan, Ronald","contributorId":291857,"corporation":false,"usgs":false,"family":"Mixan","given":"Ronald","email":"","affiliations":[{"id":62778,"text":"AZ Game and Fish Dept","active":true,"usgs":false}],"preferred":false,"id":843251,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ]}